Papers are invited on any aspect of the development of concepts,
principles, algorithms, their modelling and simulation in Artificial
Intelligence and their applications in all areas relevant to system
engineering, science, technology, business, management and industryto
be presented at AIMS2013. Conference content will be submitted for inclusion into
IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.Selected papers will be submitted for publication in the
International Journal of Simulation: Systems, Science & Technology,
IJSSST (http://ijssst.info). The event provides authors with an outstanding opportunity
for networking and presenting their work at a top quality international
conference.

AIMS2013 will be held
over 3 day period in Kota Kinabalu, state of Sabah, Island of Borneo,
Malaysia.

Exhibitors: manufacturers of software and hardware, publishers,
etc., are invited to apply to exhibit their products.

The
conference is organised and sponsored by UK Simulation Society/Asia Modelling
and Simulation Section, and (under consideration) Technically-Co-Sponsored by
IEEE Malaysia section and IEEE Region 10. Patrons, Promoters and supporters
of the conference include: University of Malaysia in Sabah, University of
Malaysia in Pahang, University of Technology Malaysia, Nottingham Trent
University, IEEE UK & RI Computer Chapter, EUROSIM and European Council
for Modelling & Simulation.

Submission
Guidelines

Submissions must be
original, unpublished work containing new and interesting results that
demonstrate current research in all areas of artificial intelligence,
modelling and simulation and their applications in science, technology,
business and commerce. Full papers only, 6 pages maximum, are accepted for
review.

Submission implies the willingness of at least one of
the authors to register and present the paper.

First, this lecture will provide
research experience such as, IM (immune system), genetic algorithm, PSO
(particle swarm optimization), BA (bacterial foraging), and its hybrid system
and application to real system. In detailed description, this lecture describes
research results about immune network based parameter estimation method for
induction motor, PSO, BA, and Hybrid based optimal selection for PID
controller through simulation and experiment in real system such as AVR and
motor vector control system.

In the conventional genetic
algorithm, it takes a long time to compute and could not include a variety of
information of plant because of using sequential computing methods. That is
some problem with making an artificial intelligence for optimization. In this
lecture, by means of introducing selection algorithm of hybrid system into
computing procedure, it will be showed advanced results. That is, it can be
calculated simultaneously necessary information, transfer function, time
constant, and etc., for plant operation condition. Therefore, computing time
is about 30% shorter than that of the conventional genetic algorithm and
10.6% smaller in overshoot when it is applied to controller.

In this lecture we will show
results of Rosenbrock function. All hybrid system have a faster computing
speed than the previous one genetic or so.

The suggested method is
applied to tuning of automatic controller for terminal voltage regulation of
AVR (automatic Voltage Regulator) of thermal power plant and motor vector
control system. Results in AVR reveal best response at 100 generations and
results show 6.8331% error in GA, 5.3828% error (78.8%: reduced) in GA-PSO,
in case of overshoot. In case of steady state error, results illustrate
reduced error with 0.0028% error (16.4%: reduced) with 0.0171% in GA and
0.0143% in GA-PSO. In settling time, it represents 0.557(sec) in GA and
0.3989(sec) in GA-PSO and it reduce to 0.159(sec) (28.5%) by using GA-PSO. In
the case of rise time, results shows 0.2037(sec) in GA and 0.2639(sec) in
GA-PSO and tuning results are better than that of conventional method.

However, we have some questions why we have
to study not introducing emotion function because emotion function can give
an impact on decision making as they mentioned earlier. So, this lecture will
mention how we can research for artificial intelligence and robot by using
studied materials up to now. Especially, to get an idea for artificial
intelligence, we strongly suggest that we had better investigate natural
system such as BA, PSO, termites, bee, and so on. Of course, robots are
becoming more and more ubiquitous in human environments as emerging
technology for economic growth. Artificial intelligence will be decided by
our ability to express effectively human’s mind such as intelligence and
emotion. That is, emotion-inspired mechanisms will deal with importance for
autonomous robots in a human environment, and also related works may be
studied.

Herein, we are going to develop the corresponding fusion algorithms or
models with learning algorithms including emotion function. Finally,
presenter would like to have question; why are you going to research AI,
Where are you going to get an idea?

Korea-Hungary Joint Work : Aug.1,2010-Feb.28,2011,
Participation in the research of Robot motion related topics of the ETOCOM
project(TAMOP4.2.2-08/1/KMR-2008-2007) including consultation with research
staff members and giving related lectures)

Particle Swarm Optimization (PSO) is a population based stochastic optimization
algorithm, inspired by the social behavior of bird flocking and fish
schooling. PSO has been introduced by Kennedy and Eberhart and contains a
group of particles that move in a search space searching for an optimum
solution according to a particular objective function. The movement of a
particle is subjected to its own best found solution, pBest, and the best
found solution in the neighborhood, gBest.

This lecture presents the latest fundamental enhancements of PSO in
asynchronous update, discrete, and multi-objective problems.

Synchronous
Asynchronous Particle Swarm Optimization

Particle swarm optimization (PSO) is one of the successful members of
swarm intelligence family. The particles in PSO look for optimal solution by updating
their velocity and position using two simple mathematical equations.
Originally, the algorithm was introduced as a synchronous update algorithm
(S-PSO), where the particles velocity and position are updated after the
whole swarm performance is evaluated.Asynchronous update in PSO has been explored recently. A particle in
asynchronous PSO (A-PSO) updates its velocity and position as soon after its
own performance is evaluated. In this paper, we attempt to improve PSO by
merging both synchronous and asynchronous update in the search process. The
proposed algorithm, which is named as, Synchronous – Asynchronous PSO
(SA-PSO), divides the particles into smaller groups. The best member of the
group and the swarm’s best are chosen to lead the search. The members of the
group are updated synchronously while the groups are asynchronously updated.
Five well known unimodal functions and four multimodal functions are used
here to study the performance of the proposed algorithm. The performance of
the algorithm is compared with three existing PSO algorithms. The results
show that the proposed algorithm is able to consistently produce good optimal
solutions.

Multi-State Particle
Swarm Optimization

The conventional binary particle swarm optimization (BPSO) algorithm is
suffering from the problem of stagnation in local optimum and complexity. In
updating current state to next state, the BPSO algorithm requires a high
dimensional bit vector. Due to these challenging problems, in recent years,
several attempts have been reported to improve BPSO algorithm. In this paper,
a multi-state particle swarm optimization algorithm for solving discrete
optimization problems is proposed. The proposed algorithm works based on a
simplified mechanism of transition between two states. In order to avoid the
repetitive states, a rule is embedded in the multi-state particle swarm
optimization algorithm. In this paper, performance of multi-state particle
swarm optimization with embedded rule (MSPSOER) is emperically compared to
original and improved BPSO algorithms based on six sets of selected
benchmarks instances of traveling salesman problem (TSP). The experimental
results showed the effectiveness of the newly introduced approach, regarding
its ability to consistently outperforms the binary-based algorithms in
solving the discrete optimization problem.

The Vector Evaluated Particle Swarm Optimisation (VEPSO) algorithm has
been widely used in solving multi-objective optimisation problems. In the
VEPSO algorithm, particles of a swarm use the best solution found by their
neighbourhood swarm to guide their movement. However, it has been found that
the VEPSO mechanism is not capable of producing good solutions for multi-objective
optimisation problems. Hence, non-dominated solutions and the concept of
multi non-dominated leaders are incorporated to improve the VEPSO algorithm.
The improved VEPSO is measured by the number of non-dominated solutions
found, Generational Distance, Spread, and Hypervolume. This analysis shows
that improved VEPSO signicantly improves upon the original VEPSO algorithm.

Biography

Dr Zuwairie Ibrahim received his B.Eng (Mechatronics) and
M.Eng. (Image Processing) from Universiti Teknologi Malaysia, in 2000 and
2002,respectively. In 2006, he has been awarded a PhD (DNA Computing) from
Meiji University, Japan. In 2002 to 2012, he was engaged with the Department
of Mechatronics and Robotics, Faculty of Eletrical Engineering, Universiti
Teknologi Malaysia, as a lecturer. Dr Zuwairie Ibrahim is currently an
Associate Professor in the Faculty of
Electrical and Electronic Engineering, Universiti Malaysia Pahang. He is
the co-author of the book entitled Bioevaluation
of World Transport Networks, published by World Scientific in2012. He has been appointed to the
Editorial of International Journal of
Simulation: Systems, Science, and Technology (IJSSST) by UK Simulation
Society and Jurnal Elektrika by
Faculty of Electrical Engineering, Universiti Technologi Malaysia. He is also
has been appointed as visiting researchers in universities in Japan and
Malaysia. He is an author/co-author of more than 50 publications in
international journals and more than 100 publications in conferences. His
research interests include computational intelligence, image processing, and
unconventional computation such as molecular or DNA computing.